# Iris flower data set

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus". Fisher's paper was published in the journal, the Annals of Eugenics, creating controversy about the continued use of the Iris dataset for teaching statistical techniques today.
The data set consists of 50 samples from each of three species of Iris. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.

## Use of the data set

Based on Fisher's linear discriminant model, this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines.
The use of this data set in cluster analysis however is not common, since the data set only contains two clusters with rather obvious separation. One of the clusters contains Iris setosa, while the other cluster contains both Iris virginica and Iris versicolor and is not separable without the species information Fisher used. This makes the data set a good example to explain the difference between supervised and unsupervised techniques in data mining: Fisher's linear discriminant model can only be obtained when the object species are known: class labels and clusters are not necessarily the same.
Nevertheless, all three species of Iris are separable in the projection on the nonlinear and branching principal component. The data set is approximated by the closest tree with some penalty for the excessive number of nodes, bending and stretching. Then the so-called "metro map" is constructed. The data points are projected into the closest node. For each node the pie diagram of the projected points is prepared. The area of the pie is proportional to the number of the projected points. It is clear from the diagram that the absolute majority of the samples of the different Iris species belong to the different nodes. Only a small fraction of Iris-virginica is mixed with Iris-versicolor. Therefore, the three species of Iris are separable by the unsupervising procedures of nonlinear principal component analysis. To discriminate them, it is sufficient just to select the corresponding nodes on the principal tree.

## Data set

The dataset contains a set of 150 records under five attributes - sepal length, sepal width, petal length, petal width and species.

 Dataset Order Sepal length Sepal width Petal length Petal width Species 1 5.1 3.5 1.4 0.2 I. setosa 2 4.9 3.0 1.4 0.2 I. setosa 3 4.7 3.2 1.3 0.2 I. setosa 4 4.6 3.1 1.5 0.2 I. setosa 5 5.0 3.6 1.4 0.3 I. setosa 6 5.4 3.9 1.7 0.4 I. setosa 7 4.6 3.4 1.4 0.3 I. setosa 8 5.0 3.4 1.5 0.2 I. setosa 9 4.4 2.9 1.4 0.2 I. setosa 10 4.9 3.1 1.5 0.1 I. setosa 11 5.4 3.7 1.5 0.2 I. setosa 12 4.8 3.4 1.6 0.2 I. setosa 13 4.8 3.0 1.4 0.1 I. setosa 14 4.3 3.0 1.1 0.1 I. setosa 15 5.8 4.0 1.2 0.2 I. setosa 16 5.7 4.4 1.5 0.4 I. setosa 17 5.4 3.9 1.3 0.4 I. setosa 18 5.1 3.5 1.4 0.3 I. setosa 19 5.7 3.8 1.7 0.3 I. setosa 20 5.1 3.8 1.5 0.3 I. setosa 21 5.4 3.4 1.7 0.2 I. setosa 22 5.1 3.7 1.5 0.4 I. setosa 23 4.6 3.6 1.0 0.2 I. setosa 24 5.1 3.3 1.7 0.5 I. setosa 25 4.8 3.4 1.9 0.2 I. setosa 26 5.0 3.0 1.6 0.2 I. setosa 27 5.0 3.4 1.6 0.4 I. setosa 28 5.2 3.5 1.5 0.2 I. setosa 29 5.2 3.4 1.4 0.2 I. setosa 30 4.7 3.2 1.6 0.2 I. setosa 31 4.8 3.1 1.6 0.2 I. setosa 32 5.4 3.4 1.5 0.4 I. setosa 33 5.2 4.1 1.5 0.1 I. setosa 34 5.5 4.2 1.4 0.2 I. setosa 35 4.9 3.1 1.5 0.2 I. setosa 36 5.0 3.2 1.2 0.2 I. setosa 37 5.5 3.5 1.3 0.2 I. setosa 38 4.9 3.6 1.4 0.1 I. setosa 39 4.4 3.0 1.3 0.2 I. setosa 40 5.1 3.4 1.5 0.2 I. setosa 41 5.0 3.5 1.3 0.3 I. setosa 42 4.5 2.3 1.3 0.3 I. setosa 43 4.4 3.2 1.3 0.2 I. setosa 44 5.0 3.5 1.6 0.6 I. setosa 45 5.1 3.8 1.9 0.4 I. setosa 46 4.8 3.0 1.4 0.3 I. setosa 47 5.1 3.8 1.6 0.2 I. setosa 48 4.6 3.2 1.4 0.2 I. setosa 49 5.3 3.7 1.5 0.2 I. setosa 50 5.0 3.3 1.4 0.2 I. setosa 51 7.0 3.2 4.7 1.4 I. versicolor 52 6.4 3.2 4.5 1.5 I. versicolor 53 6.9 3.1 4.9 1.5 I. versicolor 54 5.5 2.3 4.0 1.3 I. versicolor 55 6.5 2.8 4.6 1.5 I. versicolor 56 5.7 2.8 4.5 1.3 I. versicolor 57 6.3 3.3 4.7 1.6 I. versicolor 58 4.9 2.4 3.3 1.0 I. versicolor 59 6.6 2.9 4.6 1.3 I. versicolor 60 5.2 2.7 3.9 1.4 I. versicolor 61 5.0 2.0 3.5 1.0 I. versicolor 62 5.9 3.0 4.2 1.5 I. versicolor 63 6.0 2.2 4.0 1.0 I. versicolor 64 6.1 2.9 4.7 1.4 I. versicolor 65 5.6 2.9 3.6 1.3 I. versicolor 66 6.7 3.1 4.4 1.4 I. versicolor 67 5.6 3.0 4.5 1.5 I. versicolor 68 5.8 2.7 4.1 1.0 I. versicolor 69 6.2 2.2 4.5 1.5 I. versicolor 70 5.6 2.5 3.9 1.1 I. versicolor 71 5.9 3.2 4.8 1.8 I. versicolor 72 6.1 2.8 4.0 1.3 I. versicolor 73 6.3 2.5 4.9 1.5 I. versicolor 74 6.1 2.8 4.7 1.2 I. versicolor 75 6.4 2.9 4.3 1.3 I. versicolor 76 6.6 3.0 4.4 1.4 I. versicolor 77 6.8 2.8 4.8 1.4 I. versicolor 78 6.7 3.0 5.0 1.7 I. versicolor 79 6.0 2.9 4.5 1.5 I. versicolor 80 5.7 2.6 3.5 1.0 I. versicolor 81 5.5 2.4 3.8 1.1 I. versicolor 82 5.5 2.4 3.7 1.0 I. versicolor 83 5.8 2.7 3.9 1.2 I. versicolor 84 6.0 2.7 5.1 1.6 I. versicolor 85 5.4 3.0 4.5 1.5 I. versicolor 86 6.0 3.4 4.5 1.6 I. versicolor 87 6.7 3.1 4.7 1.5 I. versicolor 88 6.3 2.3 4.4 1.3 I. versicolor 89 5.6 3.0 4.1 1.3 I. versicolor 90 5.5 2.5 4.0 1.3 I. versicolor 91 5.5 2.6 4.4 1.2 I. versicolor 92 6.1 3.0 4.6 1.4 I. versicolor 93 5.8 2.6 4.0 1.2 I. versicolor 94 5.0 2.3 3.3 1.0 I. versicolor 95 5.6 2.7 4.2 1.3 I. versicolor 96 5.7 3.0 4.2 1.2 I. versicolor 97 5.7 2.9 4.2 1.3 I. versicolor 98 6.2 2.9 4.3 1.3 I. versicolor 99 5.1 2.5 3.0 1.1 I. versicolor 100 5.7 2.8 4.1 1.3 I. versicolor 101 6.3 3.3 6.0 2.5 I. virginica 102 5.8 2.7 5.1 1.9 I. virginica 103 7.1 3.0 5.9 2.1 I. virginica 104 6.3 2.9 5.6 1.8 I. virginica 105 6.5 3.0 5.8 2.2 I. virginica 106 7.6 3.0 6.6 2.1 I. virginica 107 4.9 2.5 4.5 1.7 I. virginica 108 7.3 2.9 6.3 1.8 I. virginica 109 6.7 2.5 5.8 1.8 I. virginica 110 7.2 3.6 6.1 2.5 I. virginica 111 6.5 3.2 5.1 2.0 I. virginica 112 6.4 2.7 5.3 1.9 I. virginica 113 6.8 3.0 5.5 2.1 I. virginica 114 5.7 2.5 5.0 2.0 I. virginica 115 5.8 2.8 5.1 2.4 I. virginica 116 6.4 3.2 5.3 2.3 I. virginica 117 6.5 3.0 5.5 1.8 I. virginica 118 7.7 3.8 6.7 2.2 I. virginica 119 7.7 2.6 6.9 2.3 I. virginica 120 6.0 2.2 5.0 1.5 I. virginica 121 6.9 3.2 5.7 2.3 I. virginica 122 5.6 2.8 4.9 2.0 I. virginica 123 7.7 2.8 6.7 2.0 I. virginica 124 6.3 2.7 4.9 1.8 I. virginica 125 6.7 3.3 5.7 2.1 I. virginica 126 7.2 3.2 6.0 1.8 I. virginica 127 6.2 2.8 4.8 1.8 I. virginica 128 6.1 3.0 4.9 1.8 I. virginica 129 6.4 2.8 5.6 2.1 I. virginica 130 7.2 3.0 5.8 1.6 I. virginica 131 7.4 2.8 6.1 1.9 I. virginica 132 7.9 3.8 6.4 2.0 I. virginica 133 6.4 2.8 5.6 2.2 I. virginica 134 6.3 2.8 5.1 1.5 I. virginica 135 6.1 2.6 5.6 1.4 I. virginica 136 7.7 3.0 6.1 2.3 I. virginica 137 6.3 3.4 5.6 2.4 I. virginica 138 6.4 3.1 5.5 1.8 I. virginica 139 6.0 3.0 4.8 1.8 I. virginica 140 6.9 3.1 5.4 2.1 I. virginica 141 6.7 3.1 5.6 2.4 I. virginica 142 6.9 3.1 5.1 2.3 I. virginica 143 5.8 2.7 5.1 1.9 I. virginica 144 6.8 3.2 5.9 2.3 I. virginica 145 6.7 3.3 5.7 2.5 I. virginica 146 6.7 3.0 5.2 2.3 I. virginica 147 6.3 2.5 5.0 1.9 I. virginica 148 6.5 3.0 5.2 2.0 I. virginica 149 6.2 3.4 5.4 2.3 I. virginica 150 5.9 3.0 5.1 1.8 I. virginica

The iris data set is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R base and Python in the machine learning package Scikit-learn, so that users can access it without having to find a source for it.

### The following R (programming language)">R">R (programming language) code illustrates usage.

iris
class
1. "data.frame"
iris3
class
1. "array"